SOTAVerified

Term Extraction

Term Extraction, or Automated Term Extraction (ATE), is about extraction domain-specific terms from natural language text. For example, the sentence “We meta-analyzed mortality using random-effect models” contains the domain-specific single-word terms "meta-analyzed", "mortality" and the multi-word term "random-effect models".

Papers

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Showing 91–100 of 160 papers

TitleStatusHype
A Case Study in Bootstrapping Ontology Graphs from Textbooks—0
D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction—0
Efficient Terminology Integration for LLM-based Translation in Specialized Domains—0
Enhancing Aspect Term Extraction with Soft Prototypes—0
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval—0
Ensembling Transformers for Cross-domain Automatic Term Extraction—0
Evaluating Automatic Term Extraction Methods on Individual Documents—0
Evaluating Term Extraction Methods for Interpreters—0
Expertise Mining for Enterprise Content Management—0
Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BaselineF1-Score0.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Seq2Seq4ATEF1-Score0.8—Unverified